An image guided algorithm for range map super-resolution

Three-dimensional imaging is increasingly becoming important in a number of applications that observe and analyze real-world environments. Range sensors, such as flash imaging Lidar and Time-of-flight camera, which can deliver high accuracy range measurement images, but are limited by the low resolution. To overcome this limitation, this paper shows the benefit of multimodal sensor system, combining a low-resolution range sensor with a high-resolution optical sensor, in order to provide a high-resolution, low-noise range image of the scene. First, an extrinsic calibration algorithm is used to align the range map with optical image. Then, an image-guided algorithm is proposed to solve the super-resolution optimization problem. This algorithm using the Markov Random Field framework. It defines an energy function that combines a standard quadratic data term and a regularizing term with the weighting factors that relate optical image edges to range map edges. Experiments on synthetic and real data are provided and analyzed to validate this method. The result confirms that the quality of the estimated high-resolution range map is improved. This work can be extended for video super-resolution with the consideration of temporal coherence.